Methods, systems, and devices for determining a respiration rate

ABSTRACT

Disclosed are methods, systems, and devices for determining a respiration rate. An example method includes capturing by a wearable device over a non-zero time period a PPG signal and making a first determination that, during the non-zero time period, the wearable device has moved less than a threshold amount. Responsive to the first determination, the method includes (i) determining from the PPG signal each of (1) a respiratory-induced intensity variation (RIIV) signal, (2) a respiratory-induced amplitude variation (RIAV) signal, and (3) a respiratory-induced frequency variation (RIFV) signal; and (ii) making a second determination that, based on the RIIV signal, the RIAV signal, and the RIFV signal, a respiration rate is determinable. Responsive to the second determination, the method includes (i) determining the respiration rate and (ii) providing by the wearable device a notification, with the notification being based at least in part on the determined respiration rate.

BACKGROUND

Unless otherwise indicated herein, the materials described in thissection are not prior art to the claims in this application and are notadmitted to be prior art by inclusion in this section.

One method for determining a heart rate of a person involves using aphotoplethysmographic (PPG) sensor. Such a sensor typically includes oneor more light sources and one or more detector. During use, the one ormore lights sources may illuminate a portion of a person's skin. Bloodflowing through vessels within the illuminated portion of the skinreflects a portion of the emitted light, which the one or more detectorsmay detect over a non-zero time period, thereby providing a PPG signal.A pulse or heart rate can thus be determined from the PPG signal, aschanges in the intensity of the detected light that correlate to changesin blood flow through the illuminated area resulting from the person'sheart pumping blood. However, a number of factors may affect theintensity of the light detected by the one or more detectors. Forinstance, a person's breathing may cause respiratory-induced variationsin a PPG signal. Assuming a regular respiration rate over the non-zerotime period in which a given PPG signal is captured, it may be possibleto estimate a respiration rate from one or more respiratory-inducedvariations in the PPG signal.

SUMMARY

In one aspect, a method is disclosed. The method includes capturing by awearable device over a non-zero time period a photoplethysmographic(PPG) signal. The method also includes making a first determinationthat, during the non-zero time period, the wearable device has movedless than a threshold amount. Responsive to the first determination, themethod includes (i) determining from the PPG signal each of (1) arespiratory-induced intensity variation (RIIV) signal, (2) arespiratory-induced amplitude variation (RIAV) signal, and (3) arespiratory-induced frequency variation (RIFV) signal; and (ii) making asecond determination that, based on at least two of the RIIV signal, theRIAV signal, and the RIFV signal, a respiration rate is determinable.Responsive to the second determination, the method includes (i)determining, from at least two of the RIIV signal, the RIAV signal, andthe RIFV signal, the respiration rate and (ii) providing by the wearabledevice a notification, with the notification being based at least inpart on the determined respiration rate.

In another aspect, a wearable device is disclosed. The wearable deviceincludes a sensor configured to capture PPG signals, a processor, and adata storage having stored therein program instructions executable bythe processor. The program instructions, when executed by the processor,cause the processor to receive from the sensor over a non-zero timeperiod a PPG signal. The program instructions also cause the processorto make a first determination that, during the non-zero time period, thewearable device has moved less than a threshold amount. Additionally,the program instructions cause the processor to, after making the firstdetermination, (a) determine from the PPG signal each of (1) an RIIVsignal, (2) an RIAV signal, and (3) an RIFV signal; and (b) make asecond determination that, based on at least two of the RIIV signal, theRIAV signal, and the RIFV signal, a respiration rate is determinable.Further, the program instructions cause the processor to, after makingthe second determination, determine a respiration rate based on the RIIVsignal, the RIAV signal, and the RIFV signal.

In still another aspect, a system is disclosed. The system comprisesmeans included in a wearable device for capturing over a non-zero timeperiod a PPG signal. The system further comprises means for making afirst determination that, during the non-zero time period, the wearabledevice has moved less than a threshold amount. The system furthercomprise means for, after making the first determination, (i)determining from the PPG signal each of (1) an RIIV signal, (2) an RIAVsignal, and (3) an RIFV signal; and (ii) making a second determinationthat, based on at least two of the RIIV signal, the RIAV signal, and theRIFV signal, a respiration rate is determinable. Additionally, thesystem comprises means for, after making the second determination, (i)determining, from at least two of the RIIV signal, the RIAV signal, andthe RIFV signal, the respiration rate and (ii) providing by the wearabledevice a notification, with the notification being based at least inpart on the determined respiration rate.

These as well as other aspects, advantages, and alternatives, willbecome apparent to those of ordinary skill in the art by reading thefollowing detailed description, with reference where appropriate to theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph of an example photoplethysmographic signal.

FIG. 2 is a block diagram of an example health monitoring system thatincludes a plurality of electronic devices in communication with aserver.

FIG. 3 is an example wearable electronic device.

FIG. 4 is a functional block diagram of components disposed in anexample wearable electronic device.

FIG. 5A is a block diagram of an example server.

FIG. 5B is a block diagram of an example cloud-based server system.

FIG. 6 is a flowchart of an example method.

FIG. 7 is a flowchart showing operations used to determine examplefrequency-domain respiratory-induced variations in aphotoplethysmographic signal.

FIGS. 8A, 8B, 8C, 8D, and 8E are graphs of example signals.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying figures, which form a part hereof. In the figures, similarsymbols typically identify similar components, unless context dictatesotherwise. The illustrative embodiments described in the detaileddescription, figures, and claims are not meant to be limiting. Otherembodiments may be utilized, and other changes may be made, withoutdeparting from the scope of the subject matter presented herein. It willbe readily understood that the aspects of the present disclosure, asgenerally described herein, and illustrated in the figures, can bearranged, substituted, combined, separated, and designed in a widevariety of different configurations, all of which are explicitlycontemplated herein.

I. OVERVIEW

Example embodiments described herein are directed to aspect of methods,systems, and devices for determining a respiration rate fromrespiratory-induced variations (RIVs) in a photoplethysmographic (PPG)signal. FIG. 1 illustrates three such RIVs in an example PPG signal 100:respiratory-induced intensity variations (RIIV), respiratory-inducedamplitude variations (RIAV), and respiratory-induced frequencyvariations (RIFV). RIIVs are variations in perfusion baseline (e.g., thepeak intensity of pulses) of a PPG signal that result from theintrathoracic pressure variations caused by the exchange of bloodbetween the pulmonary circulation and the systemic circulation.Variations in the intensities of the pulses in the PPG signal 100, asshown by a trend line 102, are example RIIVs. In contrast, RIAVs arevariations in the amplitudes of individual pulses that result from adecrease (or increase) in cardiac output due to reduced (or increased)ventricular filling. Differences in a first amplitude 104 of a firstpulse, a second amplitude 106 of a second pulse, and a third amplitude108 of a third pulse are example RIAVs. Finally, RIFVs are variations inpulse frequency caused by the autonomic response that synchronizes heartrate and respiration rate; in general, heart rate increases duringinspiration and decreases during expiration. A difference between (1) afirst time period 110 between the first pulse and the second pulse and(2) a second time period 112 between the second pulse and the thirdpulse is an example of an RIFV.

An example method includes capturing a by a wearable device over anon-zero time period a PPG signal and making a first determination thata wearable device has moved less than a threshold amount. Since amovement of the wearable device can adversely affect a PPG signal,thereby making any subsequent determination of the respiration ratepotentially unreliable, making the first determination may reducelikelihood of determining an inaccurate respiration rate. An examplewearable device may include a sensor configured to capture a PPG signalduring each of a plurality of non-zero time periods. The wearable devicemay thus be configured to be worn on a part of a user's body thatfacilitates capturing PPG signals, such as the user's wrist. Thewearable device may also include and/or be connected to one or moreadditional sensors configured to capture physiological and/ornon-physiological parameters. Examples of such physiological parametersinclude a body temperature, a galvanic skin response, an insulin level,or the like, while the non-physiological parameters may include amovement of the wearable device, a position of the wearable device, anactivity in which the user is engaging, etc.

The example method also includes, responsive to making the firstdetermination, (i) determining from the PPG signal each of (1) an RIIVsignal, (2) an RIAV signal, and (3) an RIFV signal; and (ii) making asecond determination that, from at least two of the RIIV signal, theRIAV signal, and the RIFV signal, a respiration rate is determinable.The wearable device may make the second determination by processing theRIV signals to determine from each RIV signal a preliminary respirationrate. The wearable device may then make the second determination bydetermining that at least two of the three preliminary respiration ratesare within a threshold of each other.

Alternatively, a remote computing device, such as a server or a servercluster, may make the second determination. By way of example, thewearable device may process the RIV signals to generate a vector thatincludes power levels in each of one or more frequency bands for eachRIV signal. The wearable device may send the vector to the remotecomputing device, and the remote computing device may use amachine-learning algorithm to determine from the vector whether therespiration rate is determinable. The remote computing device may thensend to the wearable device a signal indicative of whether therespiration rate is determinable.

Responsive to making the second determination, the example method thenincludes (i) determining from two or more of the RIV signals therespiration rate and (ii) causing by the wearable device an outputdevice to provide a notification, with the notification being based onthe determined respiration rate. As used herein, a notification refersto a tactile output, an audible output, a visual output, or another typeof perceptible output that a user of the wearable device, and possiblyanother person or animal (e.g., a service animal) in the vicinity of theuser, can perceive. The wearable device may perform one or morestatistical operations to determine the respiration rate. By way ofexample, the wearable device could determine the respiratory bydetermining a cross-correlation of two or more RIV signals, or thewearable device could determine the respiration rate by averaging two ormore of the preliminary respiration rates. Alternatively, the remotecomputing device could determine from the vector the respiration rateand send to the wearable device a signal that includes data indicativeof the respiration rate.

Beneficially, the methods, systems, and devices described herein mayprovide a user of a wearable device with an efficient and reliable wayto determine the user's respiration rate. Additionally, the determinedrespiration rate may allow a user, or perhaps a user's physician orother attending medical professional, to determine whether the usershows respiratory symptoms indicative of certain medical conditions. Thewearable device and/or the remote computing device may have access todata indicative of the respiration rates and/or trend in respirationrates, as well as other physiological parameters and non-physiologicalparameters, that correlate to various medical conditions. The wearabledevice and/or remote computing device may access such data to determinewhether the respiration rates and other physiological andnon-physiological parameters determined or captured over a period oftime correlates to a particular medical condition or set of medicalconditions. For instance, a negligible respiration rate while the useris sleeping may indicate that the user suffers from a sleep disorder,such as sleep apnea, while a prolonged, excessive respiratory rating(greater than about 40 breaths per minute) may be indicative of arespiratory infection such as bronchitis or pneumonia. In the event acorrelating set of data is identified, the notification may alert theuser and/or medical professionals that the user shows symptoms of theidentified medical condition.

II. EXAMPLE SYSTEMS AND SYSTEM COMPONENTS

Turning now to the figures, FIG. 2 is a simplified schematic of a healthmonitoring system 200 that includes wearable devices 220. A user maywear one of the wearable device 220 and possibly one or more remotesensors 212 that communicate with the user's wearable device 220. Eachwearable device 220 may capture and/or receive from the sensor(s) 212 aplurality of physiological parameter measurements and a plurality ofnon-physiological para parameter measurements. The user's wearabledevice 210 may also communicate with one or more output devices 214,which may provide a notification. By way of example, the notificationmay be an audible, tactile, or visual, and the one or more outputdevices 214 may include smartphones, alarm clocks, tablet computers, oranother electronic device capable of providing one or morenotifications.

The wearable devices 210 may also communicate with a server 230 via acommunication network 220, perhaps by a wireline connection and/or awireless connection. The communication network 220 may include one ormore of a plain old telephone service (POTS) network, a cellularnetwork, a fiber-optic network, or a data network. The server 230 maycommunicate with the wearable devices 210 according to one or morenetwork protocols and/or application-level protocols to facilitate theuse of network-based or cloud-based computing on client devices. Theserver 230 may include integrated data storage (e.g., memory, diskdrives, etc.) and may also be able to access a separate server datastorage 232. Communication between the server 230 and the server datastorage 232 may be direct (e.g., via a wireline or via a local wirelesscommunication link) and/or via the communication network 220. The serverdata storage 232 may store application data that is used to facilitatethe operations of applications performed by the wearable devices 210and/or the server 230.

Additionally or alternatively, the server 230 and the server datastorage 232 may store applications and application data at one or moreplaces accessible via communication network 220. These places may bedata centers containing numerous servers and storage devices. The exactphysical location, connectivity, and configuration of the server 230 andthe server data storage 232 may be unknown and/or unimportant to clientdevices (e.g., the wearable devices 210). Accordingly, the server 230and the server data storage device 232 may be referred to as“cloud-based” devices that are housed at various remote locations. Onepossible advantage of such “cloud-based” computing is to offloadprocessing and data storage from client devices, thereby simplifying thedesign and requirements of these client devices.

In some embodiments, the server 230 and the server data storage 232 maybe a single computing system residing in a single data center. In otherembodiments, the server 230 and the server data storage 232 may includemultiple computing systems in a data center, or even multiple computingsystems in multiple data centers, where the data centers are located indiverse geographic locations. For example, FIG. 2 depicts each of theserver 230 and the server data storage device 232 as potentiallyresiding in different physical locations.

In addition to receiving communications from the wearable devices 210,such as data indicative of physiological and non-physiological parametermeasurements, the server 230 may also be configured to gather and/orreceive, from either each wearable device 210 or some other source(s)(not shown), information regarding a user's overall medical history,environmental factors and geographical data. For example, the server 230and/or the server data storage 232 may include a user account for everyuser that contains the user's medical history.

Moreover, in some examples, the server 230 may be configured toregularly receive other information, such as viral illness or foodpoisoning outbreak data from the Centers for Disease Control (CDC) andweather, pollution and allergen data from the National Weather Service.Further, the server 230 may be configured to receive data regarding auser's health state from a hospital or physician. Such information maybe used in a machine-learning algorithm implemented by the server 230,which may identify vectors of physiological and/or non-physiologicalparameters corresponding to various medical conditions.

Additionally, the server 230 may be configured to gather and/or receivethe date, time of day and geographical location of each wearable device210 during each measurement period. In measuring physiologicalparameters of the user (e.g., extracted PPG waveforms), such informationmay be used to detect and monitor spatial and temporal spreading ofdiseases, which may be used to assess whether the user has a respiratoryillness. As such, the wearable devices 210 may be configured todetermine and/or provide an indication of its own location. For example,an electronic device may include a GPS system so that it can include GPSlocation information (e.g., GPS coordinates) in a communication to theserver. As another example, a wearable device may use a technique thatinvolves triangulation (e.g., between base stations in a cellular orBluetooth® network) to determine its location. Otherlocation-determination techniques are also possible. Such informationmay be useful in distinguishing parameters and data the correlate to amedical condition from parameters and data that correspond to physicalactivity, for example.

Further, some embodiments of the system may include privacy controlswhich may be automatically implemented or controlled by the user of eachwearable device 210. For example, where a user's collected data areuploaded to a cloud computing network for analysis by a clinician, thedata may be treated in one or more ways before it is stored or used, sothat personally identifiable information is removed. For example, auser's identity may be treated so that no personally identifiableinformation can be determined for the user, or a user's geographiclocation may be generalized where location information is obtained (suchas to a city, ZIP code, or state level), so that a particular locationof a user cannot be determined.

Additionally or alternatively, the user of each wearable devices 210 maybe provided with an opportunity to control whether or how the wearabledevice 210 collect information about the user (e.g., information about auser's medical history, social actions or activities, profession, auser's preferences, or a user's current location), or to control howsuch information may be used. Thus, the user may have control over howinformation is collected about him or her and used by a clinician orphysician or other user of the data. For example, a user may elect thatdata, such as health state and physiological parameters, collected fromhis or her device may only be used for generating an individual baselineand recommendations in response to collection and comparison of his orher own data and may not be used in generating a population baseline orfor use in population correlation studies.

Although only three wearable devices 210, one server 230, and one serverdata storage 232 are shown in FIG. 2, remote health monitoring system200 may include any number of each of these components. For instance,the health monitoring system 200 may include thousands of electronicdevices, thousands of servers, and/or thousands of server data storages.Further, while FIG. 2 depicts only one wearable device 210 as beingconnected to the remote sensors(s) 212 and the output device(s) 214,each wearable device 210 in the remote health monitoring system 200 maybe connected to one or more sensors and/or output devices.

In line with the discussion above, a user may wear one of the wearabledevices 210 and interact with the wearable devices 210 to determine theuser's respiration rate. Alternatively, the wearable devices 210 mayautomatically determine a respective user's respiration rate undercertain conditions, such as when the wearable device is substantiallystationary. In another example, the wearable devices 210 may exchangedata with the server 230 via to determine a respiration rate. In someexamples, the wearable devices 210 and/or the server 230 may beconfigured to provide a notification of a preliminary diagnosis of amedical condition from which a given user of one of the wearable devices210 suffers.

In one example operation, the server 230 may receive from the wearabledevices 210 a plethora of physiological parameter measurements andnon-physiological parameter measurements measured over a one or morenon-zero time intervals, such as time intervals of about 20 seconds.Whereas the physiological parameter measurements may include PPGsignals, galvanic skin measurements, and/or skin temperatures, thenon-physiological parameter measurements may include measurementsindicative of movements of the wearable device 210 and/or audiorecordings.

From the received plethora of physiological parameter measurements andnon-physiological parameter measurements, the server 230 may implement amachine-learning algorithm to determine vectors of parameters thatcorrelate to a respiration rate. To this end, the machine-learningalgorithm may be trained with the received physiological parametermeasurements and non-physiological parameter measurements, with thephysiological parameter measurements including at least a baselinerespiration rate and respiratory data extracted from PPG signals. Abaseline respiration rate may be determined by counting a number ofbreaths during a given non-zero time period, or the baseline respirationrate may be determined using another electrical or electromechanicaldevice. The server 230 may receive from the wearable device 210 aplurality of sets of RIV signals, with each set or RIV signals includingan RIAV signal, and RIIV signal, and an RIFV signal. For each set of RIVsignals, the server 230 may determine from the respective RIV signals anpreliminary respiration rate, and combine the preliminary respirationrates if at least two of the preliminary respiration rates are within athreshold of each other. Alternatively, statistical methods may be usedto combine the RIV signals in each set, perhaps by determiningcross-correlations. Each combined RIV signal may then be weighted basedon a variance of each combined RIV signals to a respective baselinerespiration rate. The machine-learning algorithm may be further trainedusing a training algorithm, such as a k nearest neighbor (KNN) algorithm(with k being a positive integer, for instance, seven).

Further, the machine-learning algorithm may be trained to determine setsof vectors that correlate to one or more respiratory or other healthconditions. The vector for a given condition may include ranges and/orthresholds for one or more physiological parameters and one or morenon-physiological parameters. The server 230 may then send thedetermined sets of vectors to the wearable devices 210. Additionally oralternatively, the server 230 may receive from the wearable devices 210one or more sets of vectors, with each vector including data indicativeof one or more physiological and/or non-physiological parameters. Theserver 230 may determine a respiration rate from each received vectorand may send data indicative of each determined respiration rate to therespective wearable device 210. Moreover, the server 230 may determinethat the vector, or perhaps a series of consecutive vectors correlatingto data captured over a longer non-zero time period (e.g., severalminutes, hours, or days) is indicative of a user having symptoms of amedical condition. In this example, the server 230 may send to awearable device 210 or another computing device (e.g., a physician'scomputing device) via the network 220 an alert and/or signal indicativeof the determined diagnosis, thereby facilitating verification andtreatment of a medical condition (e.g., a respiratory infection) thatthe user of the wearable device 210 may have.

Shown in FIG. 3 is a wearable device 300 that can automatically measurea plurality of physiological parameters of a person wearing the device.The term “wearable device,” as used in this disclosure, refers to anydevice that is capable of being worn at, on or in proximity to a bodysurface, such as at a wrist, ankle, waist, chest, or other body part. Inorder to take in vivo measurements in a non-invasive manner from outsideof the body, the wearable device 300 may be positioned on a portion ofthe body where subsurface vasculature is easily observable, thequalification of which will depend on the type of detection system used.A mount 310, such as a belt, wristband, ankle band, etc., can beprovided to mount the wearable device 300 at, on, or in proximity to thebody surface. The mount 310 may prevent the wearable device from movingrelative to the body, thereby reducing measurement error and noise. Themount 310 could take the form of a strap or band 320 that the user wearsaround a body part. Further, the mount 310 may include an adhesivesubstrate for adhering the wearable device 300 to the user's body.

The measurement platform 330 may include one or more sensors configuredto capture a measurement of at least one physiological parametermeasurement. The at least one physiological parameter could be anyparameter that may relate to the health of the person wearing thewearable device 300. For example, the wearable device 300 could beconfigured to measure blood pressure, pulse rate, respiration rate, skintemperature, Galvanic skin response, etc. The measurement platform 330may thus be disposed on the mount 310 such that the measurement platform330 is positioned on the body where subsurface vasculature is easilyobservable. When worn, an inner face 340 of the measurement platform 330may face the body surface.

In one example, the sensor(s) may generate PPG signals from which thewearable device 300 may determine one or more physiological parameters.To this end, the measurement platform 330 may include on the inner face340 a light emitter 350 and a light detector 352. Each PPG signal mayinclude data indicative of detected light at one or more wavelengths,and the wearable device may be configured to determine values for one ormore physiological parameters based on the intensity (or change inintensity) and wavelength of light received at the light detector 352.By way of example, the wearable device 300 may use the sensor todetermine a heart rate, a heart rate variability, a respiration rate, arespiration rate variability, or the like.

In other examples, the measurement platform 330 may include one or moreadditional sensors, each of which may be configured to non-invasivelymeasure one or more analytes in blood circulating in subsurfacevasculature proximate to the wearable device. In a non-exhaustive list,the measurement platform 330 may include any one of an acoustic (e.g.,piezoelectric, piezoceramic), electrochemical (voltage, impedance),thermal, mechanical (e.g., pressure, strain), magnetic, orelectromagnetic (e.g., magnetic resonance) sensor. The components of themeasurement platform 330 may be miniaturized so that the wearable devicemay be worn on the body without significantly interfering with thewearer's usual activities.

In some examples, the measurement platform 330 may further include oneor more additional signal sources, each of which may transmit aninterrogating signal that can penetrate the wearer's skin into theportion of subsurface vasculature, for example, into a lumen of thesubsurface vasculature. The interrogating signal can be any kind ofsignal that is benign to the wearer, such as electromagnetic, magnetic,optic, acoustic, thermal, mechanical, and results in a response signalthat can be used to measure a physiological parameter or, moreparticularly, that can detect the binding of the clinically-relevantanalyte to the nanoparticle conjugates.

The wearable device 300 may also include a user interface 360 via whichthe wearer of the device may receive one or more recommendations oralerts generated either from a remote server or other remote computingdevice, or from a processor within the device. The alerts could be anyindication that can be noticed by the person wearing the wearabledevice. For example, the alert could include a visual component (e.g.,textual or graphical information on a display), an auditory component(e.g., an alarm sound), and/or tactile component (e.g., a vibration).Further, the user interface 360 may include a display 362 where a visualindication of the alert or recommendation may be displayed.

FIG. 4 is a simplified block diagram illustrating example components ofthe wearable device 300. In the illustrated example, the wearable device300 includes a detection system 410, an input/output (I/O) interface420, a communication interface 430 for transmitting data to a remotesystem, and a controller 440.

The controller 440 may be provided as a computing device that includesone or more processors 450. The one or more processors 450 can beconfigured to execute computer-readable program instructions 470 thatare stored in the data storage 460 and that are executable to providethe functionality of a wearable device 300 described herein.

The data storage 460 may include or take the form of one or morenon-transitory, computer-readable storage media that can be read oraccessed by at least one processor 450. The one or morecomputer-readable storage media can include volatile and/or non-volatilestorage components, such as optical, magnetic, organic or other memoryor disc storage, which can be integrated in whole or in part with atleast one of the one or more processors 450. In some embodiments, thecomputer readable data storage 460 can be implemented using a singlephysical device (e.g., one optical, magnetic, organic or other memory ordisc storage unit), while in other embodiments, the computer readabledata storage 460 can be implemented using two or more physical devices.

The detection system 410 includes the light emitter 350, the lightdetector 352, and one or more sensors 416. In line with the descriptionabove, the light emitter 350 is configured to emit illumination into anenvironment of interest (e.g., into a portion of subsurfacevasculature), and light detector 352 is configured to detect one or moreproperties of light emitted from the portion of subsurface vasculaturein response to illumination emitted from the light emitter 352. In anon-exhaustive list, the light detector 352 may include one or more of aphotodiode, a phototransistor, a photoresistor, an active pixel sensor,a CCD, a camera, a spectrometer, an interferometer, or some other lightsensitive element configured to detect one or more properties of theemitted light.

The detection system 410 could additionally include electronicsconfigured to operate the light emitter 350 and the light detector 352.The electronics could include a high-speed analog-to-digital converter(ADC) configured to sample an output (e.g., a voltage, a current) of oneor more light-sensitive elements of the light detector 352. Additionallyor alternatively, the electronics could include analog frontendcircuitry that includes analog circuitry configured to filter, decimate,quantize, or otherwise alter and/or perform other analog operations orcomputations on the output(s) of the light detector 352 to produce anoutput electronic signal that is related to physiological properties orother parameters in the environment. This output electronic signal(s)could then be used (e.g., sampled by an ADC of a microcontroller) todetermine the cardiovascular pulse rate of a wearer.

The detection system 410 may additionally include one or more sensors416 for detecting additional or alternative properties of theenvironment of interest (e.g., for detecting physiological parameters ofa human whose body includes the environment of interest). Suchadditional detected properties could include any physiologicalparameters that may relate to the health of the person whose biologicaltissues are being measured by the wearable device 300. For example, thedetection system 410 could include detectors configured to measure bloodpressure, respiration rate, skin temperature, galvanic skin response,etc. In a non-exhaustive list, the one or more sensors 416 may includeany one of an optical sensor, an acoustic sensor, an electrochemicalsensor, a thermal sensor, a mechanical sensor, a magnetic sensor, and/oran electromagnetic sensor.

The one or more sensors 416 may include one or more devices formeasuring one or more non-physiological parameters. By way of example,the one or more sensors 416 may include an IMU, which may itself includeone or more accelerometers, gyrometers, and/or magnetometers. The IMUmay measure a velocity and/or an acceleration of the wearable device inone or more dimensions, from which the IMU (or the one or moreprocessors 450) may determine a movement of the wearable device. Inanother example, the one or more sensors 416 may include a microphone.Note that the microphone could also be a component of the I/O interface420.

The program instructions 462 stored on the data storage 460 may includeinstructions to perform any of the methods described herein. Forinstance, in the illustrated embodiment, program instructions 462include a controller module 464 and a pulse rate determination module466.

The controller module 464 can include instructions for operating thedetection system 410, for example, the light emitter 350 and the lightdetector 352. For example, the controller module 464 may operate thelight emitter 350 and the light detector 414 at a plurality of points intime to obtain a respective plurality of samples of a PPG signal. Inparticular, the controller module 464 can include instructions foroperating the light emitter 350 to emit illumination into a targetenvironment (e.g., tissue of a person) and for controlling the lightdetector 352 to detect an intensity, a wavelength, and/or otherproperties of light emitted from the environment responsive to theillumination.

The controller module 464 can also include instructions for operatingthe I/O interface 420. For example, the controller module 464 mayinclude instructions for displaying data collected by the detectionsystem 410 and analyzed by the pulse rate determination module 466.Further, the controller module 464 may include instructions to executecertain functions based on inputs accepted by the I/O interface 420,such as inputs accepted by one or more buttons or touchscreen displaysdisposed on the user interface.

The pulse rate determination module 466 may include instructions forreceiving data from and/or operating the detection system 410, analyzingthe data to determine pulse and/or respiration rates, identifyingpotential respiratory or other health conditions that the user maysuffer from based on at least the determined respiration rate, or otheranalytical processes relating to the environment proximate to thewearable device 300.

Some of the program instructions of the controller module 464 and thepulse rate determination module 466 may, in some examples, be stored ina computer-readable medium and executed by a processor located externalto the wearable device 300. For example, the wearable device 300 couldbe configured to illuminate and to receive light from portion ofbiological tissue (or to otherwise generate or obtain a plurality ofsamples of a signal of interest) and then transmit related data to aremote server, which may include a mobile device, a personal computer,the cloud, or any other remote system, for further processing (e.g., forthe determination of pulse rates and/or frequencies of oscillatingpatterns in the received light using methods described herein).

I/O interface 420 could include indicators, displays, buttons,touchscreens, head-mounted displays, microphones, and/or other elementsconfigured to present information about the wearable device 300 to auser and/or to allow the user to operate the wearable device 300.Additionally or alternatively, the wearable device 300 could beconfigured to communicate with another system (e.g., a cellphone, atablet, a computer, a remote server) and to present elements of a userinterface using the remote system. The I/O interface 420 could beconfigured to allow a user to specify some operation, function, orproperty of operation of the wearable device 300. The I/O interface 420could be configured to present a determined pulse rate of blood in aportion of subsurface vasculature or some other health state of a wearerof the wearable device 300, or to present some other information to auser. Other configurations and methods of operation of the I/O interface420 are anticipated.

Communication interface 430 may also be operated by instructions withinthe program instructions 462, such as instructions for sending and/orreceiving information via a wireless antenna, which may be disposed onor in the wearable device 300. The communication interface 430 canoptionally include one or more oscillators, mixers, frequency injectors,etc. to modulate and/or demodulate information on a carrier frequency tobe transmitted and/or received by the antenna. In some examples, thewearable device 300 is configured to indicate an output from thecontroller 440 by transmitting an electromagnetic or other wirelesssignal according to one or more wireless communications standards (e.g.,Bluetooth®, WiFi®, IRdA®, ZigBee®, WiMAX®, LTE®). In some examples, thecommunication interface 430 could include one or more wiredcommunications interfaces and the wearable device 300 could beconfigured to indicate an output from the controller 440 by operatingthe one or more wired communications interfaces according to one or morewired communications standards (e.g., USB, FireWire, Ethernet, RS-232).

The data storage 460 may further contain other data or information, suchas scattering, absorption, or other optical properties of tissues of auser of the wearable device 300, that may be useful in determining pulserates or other physiological parameters. Further, the data storage 460may contain data corresponding to pulse rate transition probabilities orother property baselines that describe expected changes incardiovascular pulse rate or other properties of biological tissuesand/or of a body. The baselines may be pre-stored on the data storage460, may be transmitted from a remote source, such as a remote server,or may be generated by the pulse rate determination module 466 itself.The pulse rate determination module 466 may include instructions forgenerating individual baselines for the user of the wearable device 300based on data collected over a certain period of time. For example, thepulse rate determination module 466 may generate a baseline set oftransition probabilities or other statistics describing expected changesin pulse rate over time based on cardiovascular pulse determined basedon PPG signals detected from portions of subsurface vasculature. Thepulse rate determination module 466 could store those baselines in thedata storage 460 for later use (e.g., to apply a forward-backward filterto a set of determined pulse rates or to perform some other filtering ordetermination related to a cardiovascular pulse). Baselines may also begenerated by a remote server and transmitted to the wearable device 300via the communication interface 430.

In some examples, obtained samples of a PPG signal or otherphysiological property or parameter of interest, determined pulse rates,or other information generated by the wearable device 300 mayadditionally be input to a cloud network and be made available fordownload by a user's physician. Analyses may also be performed on thecollected data, such as estimates of pulse rate variability, arrhythmia,determinations of post-surgical treatment or rehabilitation regimens,and/or efficacy of drug treatment regimens, in the cloud computingnetwork and be made available for download by physicians or clinicians.Further, collected information from individuals or populations of deviceusers may be used by physicians or clinicians in monitoring efficacy ofa surgical intervention or other treatment.

Turning now to FIG. 5A, a block diagram of a server in accordance withan example embodiment is shown. In particular, server 500 shown in FIG.5A can be configured to perform one or more functions of server 230and/or server data storage 232. Server 500 may include a user interface502, a communication interface 504, a processor 506, and/or data storage508, all of which may be linked together via a system bus, network, orother connection mechanism 514.

The user interface 502 may include user input devices such as akeyboard, a keypad, a touch screen, a computer mouse, a track ball, ajoystick, and/or other similar devices, now known or later developed.The user interface 502 may also include user display devices, such asone or more cathode ray tubes (CRT), liquid crystal displays (LCD),light emitting diodes (LEDs), displays using digital light processing(DLP) technology, printers, light bulbs, and/or other similar devices,now known or later developed. Additionally, the user interface 502 maybe configured to generate audible output(s), via a speaker, speakerjack, audio output port, audio output device, earphones, and/or othersimilar devices, now known or later developed. In some embodiments, theuser interface 502 may include software, circuitry, or another form oflogic that can transmit data to and/or receive data from external userinput/output devices.

The communication interface 504 may include one or more wirelessinterfaces and/or wireline interfaces that are configurable tocommunicate via a network, such as network 220 shown in FIG. 2. Thewireless interfaces, if present, may include one or more wirelesstransceivers, such as a BLUETOOTH® transceiver, a WiFi® transceiverperhaps operating in accordance with an IEEE 802.11 standard (e.g.,802.11b, 802.11g, 802.11n), a WiMAX transceiver perhaps operating inaccordance with an IEEE 802.16 standard, a Long-Term Evolution (LTE)transceiver perhaps operating in accordance with a 3rd GenerationPartnership Project (3GPP) standard, and/or other types of wirelesstransceivers configurable to communicate via local-area or wide-areawireless networks. The wireline interfaces, if present, may include oneor more wireline transceivers, such as an Ethernet transceiver, aUniversal Serial Bus (USB) transceiver, or similar transceiverconfigurable to communicate via a twisted pair wire, a coaxial cable, afiber-optic link or other physical connection to a wireline device ornetwork. Other examples of wireless and wireline interfaces may exist aswell.

The processor 506 may include one or more general purpose processors(e.g., microprocessors) and/or one or more special purpose processors(e.g., digital signal processors (DSPs), graphical processing units(GPUs), floating point processing units (FPUs), network processors, orapplication specific integrated circuits (ASICs)). The processor 506 maybe configured to execute computer-readable program instructions 510 thatare contained in data storage 508, and/or other instructions, to carryout various functions described herein.

Thus, the data storage 508 may include one or more non-transitorycomputer-readable storage media that can be read or accessed by theprocessor 506. The one or more computer-readable storage media mayinclude volatile and/or non-volatile storage components, such asoptical, magnetic, organic or other memory or disc storage, which can beintegrated in whole or in part with the processor 506. In someembodiments, the data storage 508 may be implemented using a singlephysical device (e.g., one optical, magnetic, organic or other memory ordisc storage unit), while in other embodiments, the data storage 508 maybe implemented using two or more physical devices.

Data storage 508 may also include the program data 512 that can be usedby processor 506 to carry out functions described herein. In someembodiments, the data storage 508 may include, or have access to,additional data storage components or devices (e.g., cluster datastorages described below).

FIG. 5B depicts a cloud-based server in accordance with an exampleembodiment. In FIG. 5B, functions of server 230 and server data storagedevice 232 may be distributed among three server clusters 520A, 520B,and 520C. Server cluster 520A may include one or more servers 500A,cluster data storage 522A, and cluster routers 524A connected by a localcluster network 526A. Similarly, server cluster 520B may include one ormore servers 500B, cluster data storage 522B, and cluster routers 524Bconnected by a local cluster network 526B. Likewise, server cluster 520Cmay include one or more servers 500C, cluster data storage 522C, andcluster routers 524C connected by a local cluster network 526C. Servers520A, 520B, and 520C may communicate with network 220 via communicationlinks 528A, 528B, and 528C, respectively.

In some embodiments, each of the server clusters 520A, 520B, and 520Cmay have an equal number of servers, an equal number of cluster datastorages, and an equal number of cluster routers. In other embodiments,however, some or all of the server clusters 520A, 520B, and 520C mayhave different numbers of servers, different numbers of cluster datastorages, and/or different numbers of cluster routers. The number ofservers, cluster data storages, and cluster routers in each server maydepend on the computing task(s) and/or applications assigned to eachserver.

In the server cluster 520A, for example, server 500A can be configuredto perform various computing tasks of server 230. In one embodiment,these computing tasks can be distributed among one or more of servers500A. Servers 500B and 500C in server clusters 520B and 520C may beconfigured the same or similarly to server 500A in server cluster 520A.On the other hand, in some embodiments, server clusters 500A, 500B, and500C each may be configured to perform different functions. For example,server cluster 500A may be configured to perform one or more functionsof server 230, and server clusters 500B and server 500C may beconfigured to perform functions of one or more other servers. Similarly,the functions of server data storage device 232 can be dedicated to asingle server cluster, or spread across multiple server clusters.

Cluster data storages 522A, 522B, and 522C of the servers 520A, 520B,and 520C, respectively, may be data storage arrays that include diskarray controllers configured to manage read and write access to groupsof hard disk drives. The disk array controllers, alone or in conjunctionwith their respective servers, may also be configured to manage backupor redundant copies of the data stored in cluster data storages toprotect against disk drive failures or other types of failures thatprevent one or more servers from accessing one or more cluster datastorages.

Similar to the manner in which the functions of server 230 and serverdata storage device 232 can be distributed across server clusters 520A,520B, and 520C, various active portions and/or backup/redundant portionsof these components can be distributed across cluster data storages522A, 522B, and 522C. For example, some cluster data storages 522A,522B, and 522C may be configured to store backup versions of data storedin other cluster data storages 522A, 522B, and 522C.

Cluster routers 524A, 524B, and 524C in servers 520A, 520B, and 520C,respectively, may include networking equipment configured to provideinternal and external communications for the servers. For example,cluster routers 524A in server 520A may include one or morepacket-switching and/or routing devices configured to provide (i)network communications between servers 500A and cluster data storage522A via cluster network 526A, and/or (ii) network communicationsbetween the server cluster 520A and other devices via communication link528A to communication network 220. Cluster routers 524B and 524C mayinclude network equipment similar to cluster routers 524A, and clusterrouters 524B and 524C may perform networking functions for serverclusters 520B and 520C that cluster routers 524A perform for servercluster 520A.

Additionally, the configuration of cluster routers 524A, 524B, and 524Ccan be based at least in part on the data communication requirements ofthe servers and cluster storage arrays, the data communicationscapabilities of the network equipment in the cluster routers 524A, 524B,and 524C, the latency and throughput of the local cluster networks 526A,526B, 526C, the latency, throughput, and cost of the wide area networkconnections 528A, 528B, and 528C, and/or other factors that maycontribute to the cost, speed, fault-tolerance, resiliency, efficiencyand/or other design goals of the system architecture.

III. EXAMPLE METHODS

Turning now to FIG. 6, a flow diagram of an example method 600 is shown.A component of a health monitoring system, such one or more of thewearable devices and/or servers described herein, may implement thefunctions of the method 600 to determine a respiration rate for one ormore users of one of the wearable devices. Alternatively, multiplecomponents of the health monitoring system may implement the functionsof the method 600. Functions described in blocks of the flowchart may beprovided as instructions stored on a non-transitory computer readablemedium that can be executed by a computing system to perform thefunctions.

In one example, a user of a wearable device may initiate the method 600by interacting with a user interface component of the wearable device.In another example, the wearable device may automatically initiate themethod 600 at specific times. For instance, the wearable device mayperform one or more steps of the method 600 at specific times of day orafter an interval of time has elapsed from the last performance of themethod 600. Additionally or alternatively, the wearable device couldperform the method 600 after capturing a non-physiological parameter. Byway of example, the wearable device may automatically perform one ormore functions the method 600 upon determining that the wearable devicehas moved less than a threshold amount.

Beginning at block 602, the method 600 includes capturing by a wearabledevice over a non-zero time period a PPG signal. The non-zero timeperiod may be about 20 seconds, though longer or shorter time periodsare possible as well. At block 604, the method 600 includes a firstdecision point at which a first determination is made as to whether thewearable device during the capture of the PPG signal has moved less thana threshold amount. The wearable device may make the first determinationbased on data received during the non-zero time period from a sensorincluded in the wearable device that is configured to capture a movementof the wearable device, such as an IMU. In line with the abovediscussion, making the first determination may improve the reliabilityof determined respiration rates, as movements of the wearable deviceduring the non-zero time period could distort the PPG signal and/orcould introduce variations in the PPG signal that are similar to RIVs.Determining that the wearable device has moved less than the thresholdamount (e.g., a movement in any given direction is less than about 1centimeter) may indicate that the wearable device is substantiallystationary, in which case any variations in the PPGs signal are likelydue to RIVs.

If the first determination is that wearable device has not moved lessthan the threshold amount, then the method 600 includes returning toblock 602. In some examples, the wearable device may output anotification responsive to the first determination being that thewearable device has not moved less than the threshold amount. Forinstance, the notification may be a visual message and/or an audiomessage indicating that the wearable device cannot determine the user'srespiration rate. Further, the visual/audio message may include arequest that the user minimize movement of the wearable device tofacilitate determination of the respiration rate.

In some examples, the wearable device may not automatically captureanother PPG signal upon returning to block 602. Instead, the wearabledevice may wait for a particular condition to occur before returning toblock 602, such as an interaction received from a user input device, anamount of time elapsing from the last PPG signal being captured, or anindication that over another non-zero time period the wearable devicehas moved less than the threshold amount (e.g., indicating that thewearable device is substantially stationary).

On the other hand, if the first determination is that the wearabledevice has moved less than the threshold amount, then the method 600continues at block 606 with determining from the PPG signal each of anRIIV signal, an RIAV signal, and an RIFV signal. Next at block 608, themethod 600 includes a second decision point. Here, a seconddetermination is made as to whether a respiration rate (RR) isdeterminable. In some instances, differences in the RIV signals mayrender unreliable a respiration rate determined from the RIV signals.Making the second determination may thus serve as an additionalsafeguard against providing the user (or a medical professional, forinstance) with an unreliable respiration rate.

In one example, the wearable device may make the second determination.By way of example, the wearable device may make the second determinationbased on a plurality of preliminary respiration rates. By way ofexample, the plurality of preliminary respiration rates includes apreliminary respiration rate determined from at least RIV signals. Inorder to make the second determination, the wearable device may firstdetermine a frequency-domain signal from each of the RIIV signal, theRIAV signal, and the RIFV signal. FIG. 7 shows a flowchart fordetermining frequency-domain RIV signals.

As shown, the wearable device may provide a filtered PPG signal byfiltering a PPG signal with a band-pass filter. A lower cutoff frequencyof the band-pass filter may remove from the PPG signal a DC component,while an upper cutoff frequency may remove noise that can adverselyaffect determination of the RIAV signal and the RIFV signal. By way ofexample, the lower cutoff frequency may be about 0.35 Hertz, and theupper cutoff frequency may be about 3 Hertz, though other upper and/orlower cutoff frequencies may also be used.

Next, the wearable device may perform a peak detection algorithm todetermine a value for each maximum and minimum value of the PPG signal,thereby providing a peak-detected signal. The wearable device may useany method, algorithm, or process now known or later developed that issuitable for identifying peaks in periodic or quasi-periodic signals. Byway of example, FIG. 8A shows an example PPG signal 800, and FIG. 8Bshows an example peak-detected signal 802.

From the peak-detected signal, the wearable device may determine an RIAVsignal by calculating an amplitude of each pulse (e.g., maximum minusminimum), while determining the RIFV signal by determining the timebetween each pulse (e.g., peak to peak) of the PPG signal. The wearabledevice may next resample each of the RIAV signal and the RIFV signal toprovide a resampled RIAV signal and a resampled RIFV signal,respectively. As one non-limiting example, the wearable device mayperform the resampling by using any suitable linear interpolation methodto resample on a 4 Hertz grid each of the RIAV signal and the RIFVsignal. FIG. 8C shows an example RIAV signal 804 and a resampled RIAVsignal 806, while FIG. 8D shows an example RIFV signal 808 and aresampled RIFV signal 810. Note that the dots on shown on the resampledsignals 804, 808 correspond to a point determined by linearinterpolation. In some examples, the wearable device may use a differentgrid for linear interpolation (e.g., a frequency other than 4 Hertz), ora different resampling technique may be used. The wearable device maythen determine a frequency-domain RIAV signal and a frequency-domainRIFV signal by performing a fast Fourier transform (FFT) on theresampled RIAV signal and the resampled RIFV signal, respectively.

With respect to the RIIV signal, the wearable device may determine thatthe PPG signal is the RIIV signal. In this example, the wearable devicemay determine a frequency-domain RIIV signal by performing an FFT on thePPG signal. Note that in some examples, the wearable device maydetermine the RIIV signal by filtering the PPG signal and/or byresampling the PPG signal prior to determining the FFT of the RIIVsignal. By way of example, the wearable device may use linearinterpolation to resample the PPG signal on a 4 Hertz grid, thoughanother resampling frequency and/or grid may be used as well. Note thatthe same resampling technique (and frequency grid) should be used toresample each RIV signal when the wearable device performs suchresampling.

The wearable device may determine the preliminary respiration rates byidentifying peaks in the frequency-domain RIV signals that are within arange of respiratory frequencies, e.g., between 0 and 1 Hertz. By way ofexample, FIG. 8E shows a graph of example frequency-domain RIV signals.As shown, a frequency-domain RIIV signal 812 may have a peak at about0.23 Hertz (i.e., breaths per second), a frequency-domain RIFV signal814 may have a peak at about 0.20 Hertz, and a frequency-domain RIAVsignal 816 may have a peak at about 0.21 Hertz. Thus, the wearabledevice may determine from the preliminary respiration rate for each ofthe frequency-domain RIV signals 812, 814, and 816 as being 0.23 Hertz,0.20 Hertz, and 0.21 Hertz, respectively.

In one example, the wearable device may make the second determinationwhen at least two of the preliminary respiration rates are within apredetermined threshold of each other (e.g., the difference between anytwo preliminary respiration rates is within than a predeterminedthreshold difference). Alternatively, the wearable device may make thesecond determination when all of the preliminary respiration rates arewithin a predetermined threshold to each other (e.g., the differencebetween the highest preliminary respiration rate and the lowestpreliminary respiration is within a predetermined threshold difference).In each of these examples, the value of the predetermined threshold maydepend on the desired correspondence between the preliminary respirationrates. As a non-limiting example, the predetermined threshold could bebetween about 0.1 Hertz and about 1 Hertz.

Alternatively, a server connected to the wearable device via a wired orwireless connection may make the second determination. Here, thewearable device may generate a vector that includes data indicative ofan energy in each of N frequency bins for each frequency-domain RIVsignal, which are shown in FIG. 8E. Here, the resulting vector V_(RIV)can be represented as follows:V _(RIV) =[E _(RIIV-B1) , . . . ,E _(RIAV-BN) ,E _(RIAV-B1) , . . . ,E_(RIAV-BN) ,E _(RIFV-B1) , . . . ,E _(RIFV-BN)]where each value of E is an energy of an RIV signal in one of binsB1-BN. For each frequency-domain RIV signal, the energy E in a given binmay be an average energy, a maximum energy, a minimum energy, or someother statistic of the energy in the bin. In one example, the server maydetermine the vector V_(RIV), in which case the wearable device maydigitize the PPG signal and send the PPG signal to the server. Theserver may determine the vector from the received PPG signal, perhaps byperforming the functions described with respect to FIG. 7. In any event,the server may use a machine-learning algorithm to make the seconddetermination based on the vector V_(RIV). For instance, if themachine-learning algorithm uses a KNN-regression, then the server maymake the second determination based on the k nearest neighbors in adatabase to the vector V_(RIV). As noted above, the value of k maydepend on the size of the database accessed by the machine-learningalgorithm, as well as other factors such as the amount of time needed tomake the determination, processing resources, etc.

Returning to FIG. 6, if the second determination is that the respirationrate is not determinable, then the method 600 includes returning toblock 602 in a similar manner as described with respect to the firstdetermination being that the wearable device has not moved less than thepredetermined amount.

On the other hand, if the second determination is that the respirationrate is determinable, then the method 600 includes determining therespiration rate at block 610. In some examples, the wearable device mayperform the functions of block 610 by combining two or more preliminaryrespiration rates. To this end, the wearable device may average thepreliminary respiration rates, or the wearable device may average thepreliminary respiration rates that are within a predetermined thresholdof each other. In the latter case, the wearable device may omit from theaverage a preliminary respiration rate that is not within thepredetermined threshold from the other preliminary respiration rates. Asanother example, the wearable device may determine a cross-correlationof two or more frequency-domain RIV signals, thereby providing across-correlated signal. Here, a peak in the cross-correlated signalbetween 0 Hertz and 1 Hertz may be correlated to the respiration rate.And in yet other examples, the wearable device may determine that therespiration rate is one of a minimum preliminary respiration rate, amaximum preliminary respiration rate, or a median preliminaryrespiration rate.

In other examples, the server may perform the functions of block 610.Here, the server may use the machine-learning algorithm to determinefrom the vector R_(RIV) the respiration rate, and the server may send tothe wearable device data indicative of the determined respiration rate.Alternatively, the server may determine the respiration rate byemploying any of the operations described in the examples in which thewearable device performs the functions of block 610.

At block 612, the method 600 includes the wearable device causing anoutput device to provide a notification. By way of example, the wearabledevice may cause an output component of a user interface device toprovide an audio or visual notification. In this case, the wearabledevice may convert the determined respiration rate from breaths persecond to breaths per minute. In the example described with respect toFIG. 8E, for instance, the wearable device may determine that therespiration rate is about 12.6 breaths per minute.

In another example, the notification may alert the user of the presenceof symptoms indicative of the user potentially having a respiratorycondition. In this case, the wearable device may use other physiologicaland non-physiological data to determine whether the respiration rate maybe a symptom of a medical condition. For instance, the wearable devicemay receive from one sensor data indicative of the user's bodytemperature, and the wearable device may also receive data from anothersensor indicative of the user's activity level (e.g., whether the userhas been running or been sedentary over a period of time preceding thecapture of the PPG signal). Moreover, the wearable device may store in adata storage data indicative of a number of previous, consecutivelydetermined respiration rates and temporally associated physiological andnon-physiological parameters. Rather than provide a notification of apotential illness based on one determined respiration rate, the wearabledevice may first determine whether a trend in respiration rates,physiological parameters, and non-physiological parameters over a longerperiod of time (perhaps several dozen sets of data taken over minutes,hours, days, etc.) is indicative of the user potentially suffering froman illness. If the trend is indicative of the user suffering from arespiratory illness, then the notification may alert the user of thepotential illness and possibly advise the user to consult a medicalprofessional.

Further, the wearable device may send a message to a computing deviceoperated by a physician (or other medical professional) alerting thephysician of the user potentially suffering from an illness. The messagemay be an email, a text message, an instant message, or the like thatincludes data indicative of the physiological data (included thedetermined respiration rate(s)) and non-physiological data used by thewearable device to determine that the user might have a respiratoryillness. In this manner, the physician may follow up with the user toschedule an appointment, or perhaps direct the user to seek immediatemedical attention, depending on the physician's assessment of data.

IV. CONCLUSION

Where example embodiments involve information related to a person or adevice of a person, the embodiments should be understood to includeprivacy controls. Such privacy controls include, at least, anonymizationof device identifiers, transparency, and user controls, includingfunctionality that would enable users to modify or delete informationrelating to the user's use of a product.

Further, in situations in where embodiments discussed herein collectpersonal information about users, or may make use of personalinformation, the users may be provided with an opportunity to controlwhether programs or features collect user information (e.g., informationabout a user's medical history, social network, social actions oractivities, profession, a user's preferences, or a user's currentlocation), or to control whether and/or how to receive content from thecontent server that may be more relevant to the user. In addition,certain data may be treated in one or more ways before it is stored orused, so that personally identifiable information is removed. Forexample, a user's identity may be treated so that no personallyidentifiable information can be determined for the user, or a user'sgeographic location may be generalized where location information isobtained (such as to a city, ZIP code, or state level), so that aparticular location of a user cannot be determined. Thus, the user mayhave control over how information is collected about the user and usedby a content server.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other embodiments may includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements may be combined or omitted. Yet further, anexemplary embodiment may include elements that are not illustrated inthe Figures.

Additionally, while various aspects and embodiments have been disclosedherein, other aspects and embodiments will be apparent to those skilledin the art. The various aspects and embodiments disclosed herein are forpurposes of illustration and are not intended to be limiting, with thetrue scope and spirit being indicated by the following claims. Otherembodiments may be utilized, and other changes may be made, withoutdeparting from the spirit or scope of the subject matter presentedherein. It will be readily understood that the aspects of the presentdisclosure, as generally described herein, and illustrated in thefigures, can be arranged, substituted, combined, separated, and designedin a wide variety of different configurations, all of which arecontemplated herein.

What is claimed is:
 1. A wearable device comprising: a sensor configuredto capture photoplethysmographic (PPG) signals; a processor; acommunication interface, wherein the communication interface isconfigured to connect the wearable device to a remote computing device;and a controller, configured to: (i) receive from the sensor over anon-zero time period a PPG signal; (ii) make a first determination that,during the non-zero time period, the wearable device has moved less thana threshold amount; (iii) in response to the first determination that,during the non-zero time period, the wearable device has moved less thanthe threshold amount, determine from the PPG signal each of (1) arespiratory-induced intensity variation (RIIV) signal, (2) arespiratory-induced amplitude variation (RIAV) signal, and (3) arespiratory-induced frequency variation (RIFV) signal; (iv) determine ineach of a plurality of frequency bins a power level of each of the RIIVsignal, the RIAV signal, and the RIFV signal; (v) send to the remotecomputing device via the communication interface data indicative of eachdetermined power level; (vi) receive from the remote computing devicevia the communication interface a signal that includes data indicativeof whether the respiration rate is determinable; and (vii) if the signalindicates that the respiration rate is determinable, determine arespiration rate based on at least two of the RIIV signal, the RIAVsignal, and the RIFV signal.
 2. The wearable device of claim 1, whereinthe controller is further configured to determine a plurality ofpreliminary respiration rates, wherein each preliminary respiration isbased on at least one of the RIIV signal, the RIAV signal, and the RIFVsignal.
 3. The wearable device of claim 2, wherein determining theplurality of preliminary respiration rates comprises identifying a peakin at least one of the RIIV signal, the RIAV signal, or the RIFV signal,wherein a frequency of each identified peak is within a range ofrespiration frequencies.
 4. The wearable device of claim 2, whereindetermining the respiration rate comprises combining at least twopreliminary respiration rates that are within a threshold of each other.5. The wearable device of claim 2, wherein determining the respirationrate comprises combining all of the preliminary respiration rates. 6.The wearable device of claim 2, wherein determining the preliminaryrespiration rate from each of the RIIV signal, the RIAV signal, and theRIFV signal comprises identifying a peak in the respectiverespiratory-induced signal in a range of respiration frequencies.
 7. Thewearable device of claim 1, wherein the controller is further configuredto generate a vector that includes each determined power level andwherein sending data to the remote computing device indicative of eachdetermined power level comprises sending the vector.
 8. The wearabledevice of claim 1, further comprising an output component to provide anotification, wherein the controller is further configured to providethe notification, and wherein the notification is based on thedetermined respiration rate.
 9. The wearable device of claim 8, whereinthe notification includes at least one of a visual output or an auditoryoutput.
 10. The wearable device of claim 1, wherein the sensor comprisesa light emitter and a light detector.
 11. The wearable device of claim1, further comprising: a mount, wherein the mount secures the device toan external body surface.
 12. The wearable device of claim 11, whereinthe external body surface is a location on a wrist.
 13. The wearabledevice of claim 11, wherein the mount comprises a band that can enclosethe wrist.
 14. The wearable device of claim 11, wherein the mountcomprises an adhesive substrate.
 15. The wearable device of claim 1,further comprising: a user interface, wherein the controller is furtherconfigured to cause the user interface to indicate information relatingto the determined respiration rate.
 16. The wearable device of claim 15,wherein the user interface comprises a display.
 17. The wearable deviceof claim 1, further comprising a second sensor configured to capturemovement data of the wearable device, and wherein the controller isfurther configured to: receive, from the second sensor and over thenon-zero time period, movement data of the wearable device.
 18. Thewearable device of claim 17, wherein the second sensor comprises one ormore accelerometers, gyrometers, and/or magnetometers.